# Reflective Guidance

Autonomous-driving vision-language-action models have learned from a scalar trajectory score that flags a bad plan but cannot say whether scene understanding, behavior selection, or trajectory execution went wrong. Reflect-VLA treats reflective guidance, which pairs evaluation diagnoses with corrective suggestions, as training-time privileged information and internalizes it through dual-path on-policy self-distillation: risky trajectories align with the teacher's token-level corrective distribution, while reliable ones are preserved by self-imitation. It reaches competitive planning performance on NAVSIM v1/v2 and Bench2Drive, cuts training time by over 90% versus reinforcement learning, and generates trajectories in a single pass at inference without guidance or iterative refinement.

Autonomous-driving vision-language-action models have learned from a scalar trajectory score that flags a bad plan but cannot say whether scene understanding, behavior selection, or trajectory execution went wrong. Reflect-VLA treats reflective guidance, which pairs evaluation diagnoses with corrective suggestions, as training-time privileged information and internalizes it through dual-path on-policy self-distillation: risky trajectories align with the teacher's token-level corrective distribution, while reliable ones are preserved by self-imitation. It reaches competitive planning performance on NAVSIM v1/v2 and Bench2Drive, cuts training time by over 90% versus reinforcement learning, and generates trajectories in a single pass at inference without guidance or iterative refinement.